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India is one of the largest real-money gaming markets, and Popunder delivers huge volumes there at a low CPM. The catch: cheap registrations rarely equal profitable players.
In this case, the Galaksion team ran an Android Popunder campaign for a real-money gaming offer in India and cut the cost per FTD by 39%: from ~$50 to $30.65. The key was simple — stop optimizing for registrations and start optimizing for first-time deposits. Note: the advertiser is kept anonymous. The text below is told from the Galaksion team’s perspective.
Galaksion is an ad network with direct Popunder traffic and source-level optimization tools. New advertisers get a 15% bonus on the first deposit with promo code AFFROOM15. Sign up here.
Popunder Gambling Case Study: Campaign Details
- Period: May 1–31 (14 days of active optimization)
- Traffic source: Galaksion
- Ad format: Popunder
- Pricing model: CPM
- Vertical: Gambling / Real-money gaming
- GEO: India
- Targeting: Mobile, Android
- Daily budget cap: $50–70
- Total spend: ~$600
- Average CPM: ~$0.40
Popunder doesn’t rely on banner creatives, so all the work went into traffic quality: sources, rates, frequency and postback data.
Results at a Glance
- Impressions: ~1.5M
- Registrations: ~315
- Depositors: ~21
- FTDs: ~15
- FTD cost: ~$50 → $30.65 (−39%)
Here’s how we got there, step by step.
The Challenge: Registrations Didn’t Tell the Whole Story
The campaign brought registrations from day one. A registration cost roughly $1.90 — looks great on paper. But only about 6–7% of registered users went on to deposit.
Some sources delivered cheap sign-ups and almost no deposits afterwards. If we had optimized for registration CPA, the budget would have flowed straight into those sources: efficient at step one, useless for the advertiser’s real goal.
The second problem was tracking. At launch, the postback didn’t separate deposit types. As the campaign progressed, we expanded it to pass deeper events, which let us optimize for first-time depositors instead of generic conversions.
The core question: which sources bring users who actually deposit — and how much more traffic can we buy from them without pushing acquisition cost too high?
How We Optimized: 6 Steps from Registrations to FTDs
Step 1. Launch broad and let the data filter the traffic
We started with Android Popunder traffic on CPM and gave the campaign room to collect data across many sources. Instead of guessing a whitelist before launch, we let live conversion history narrow the pool.
Every source was judged on three signals: registrations, deposits and conversion cost. The decision rules:
| What the source shows | Action |
|---|---|
| Spend, no conversions | Blacklist candidate |
| Registrations, no deposits | Lower priority or blacklist |
| Deposits at excessive cost | Lower the rate, collect more data |
| Stable deposits at acceptable cost | Keep, consider for whitelist |
Step 2. Cut inefficient traffic with a blacklist
We didn’t lock the campaign into a strict whitelist. Instead, we blacklisted underperforming zones and kept access to broad volume.
Zones that gathered enough data but brought no deposits, or showed consistently high acquisition cost, were removed. Sources with stable downstream performance stayed active and could be scaled one by one.
This kept the campaign from becoming too narrow and losing valuable traffic. For the strongest sources, we set custom rates and frequency separately, increasing their share without moving the whole campaign to a whitelist.
Step 3. Scale proven sources with custom rates
Once we found sources that consistently generated deposits, the goal was to get more volume from them without raising the bid everywhere.
Rather than increasing CPM campaign-wide, we applied custom rates to selected high-performing zones. Increases were gradual, usually 10–20% at a time, and we rechecked acquisition cost after each step before the next one.
Scaling logic: proven source + deposits + acceptable cost → higher custom rate → more traffic → re-evaluate.
Step 4. Test frequency as a second scaling lever
We started conservatively at about 3 impressions per 24 hours, then raised it to 4/24 on selected proven sources.
The idea: squeeze more volume out of inventory that had already proven its quality, before opening the campaign to lots of new, unknown traffic.
Step 5. Go deeper than zone-level optimization
As data grew, we analyzed performance by browser, user category and location within India. The rule stayed the same at every level: volume alone means nothing.
Segments with registrations but weak deposits were cut back. Combinations with stronger downstream conversion stayed in rotation.
Step 6. Switch the target from conversions to FTDs
The expanded postback let us separate first-time deposits (FTD), repeat deposits (RD) and unique repeat deposits (uRD). That changed the optimization target:
- a registration = initial interest;
- a deposit = stronger intent;
- an FTD = a new paying user acquired.
From this point, sources generating FTDs got top scaling priority.
Results: FTD Cost Down 39%
After 14 days of active optimization, the campaign moved from broad traffic buying to a focused pool of proven inventory.
| Metric | Result |
|---|---|
| Impressions | ~1.5M |
| Registrations | ~315 |
| Depositors | ~21 |
| Registration → deposit CR | ~6–7% |
| FTDs | ~15 |
| Initial FTD cost | ~$50 |
| Best optimized FTD cost | $30.65 |
| FTD cost improvement | ~39% |
| Blended FTD cost | ~$40 |
The biggest win was FTD cost: from about $50 at the start to $30.65 on the best-optimized traffic. The blended FTD cost for the whole period stayed around $40, while the strongest source combinations delivered much better economics.
Key Takeaways
- Optimize beyond registration CPA. A cheap sign-up isn’t efficient acquisition if the user never deposits.
- Scale sources, not campaign averages. Raise rates on proven zones first instead of lifting CPM across all inventory.
- Use frequency as a controlled test. Give winning traffic more room before buying unknown inventory.
- Keep whitelists and blacklists dynamic. Retest promising zones from time to time, but judge them on fresh deposit data.
- Pass deep events through postback. FTD-level visibility makes traffic quality decisions far more precise.
FAQ
Does Popunder work for gambling offers in India?
Yes, if you optimize for deposits rather than sign-ups. In this case, Android Popunder on CPM brought ~315 registrations and ~15 FTDs on a ~$600 budget. For other strong markets, see Top GEOs for Gambling Offers.
What is FTD cost and why optimize for it?
FTD cost is what you pay to acquire a user who makes a first deposit. It reflects real advertiser value, while registration cost only reflects interest.
How fast should you raise rates on winning zones?
Gradually: 10–20% per step, then recheck acquisition cost before the next increase.
Which postback events should you pass for gambling campaigns?
At minimum, registration and FTD. Repeat deposits (RD) and unique repeat deposits (uRD) help separate one-time payers from retained players.
Where else can you buy gambling traffic?
Compare options in our list of the best gambling ad networks and pick offers from the gambling offer base.
Ready to try this strategy yourself?
This campaign ran on Galaksion. Register with promo code AFFROOM15 and get a 15% bonus on your first deposit.
